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CBSN: Comparative measures of normalization techniques for brain tumor segmentation using SRCNet

  • 1176: Artificial Intelligence and Deep Learning for Biomedical Applications
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Abstract

The segmentation of tumors in the brain MRI scans is a difficult job for doctors and radiologists. The segmentation done by different medical experts may also have differences in their opinion for the segmented region, which is popularly known as regions of interest (ROIs). To date, researchers and academicians have proposed several approaches and frameworks for semi- and full-automatic segmentation techniques to identify ROIs accurately. It is prevalent that automatic segmentation gives comparable or even better results compared to human experts for several publicly known and privately collected datasets. Additionally, these are beneficial in those areas where doctors and radiologists’ availability is either uneven or scarce because of geographical dispersion. The convolutional neural networks (CNN) are considered for segmentation for ROIs due to their wide popularity. They have outperformed humans over tasks like object identification and image classification. The publicly available datasets or those collected from different medical institutions may have different statistics, resolution, and properties. Therefore, pre-processing has an essential role in achieving better and accurate delineation and segmentation of tumors. In the proposed work, CBSN, we consider well-known normalization techniques such as Gaussian Mixture Models (GMM), Fuzzy C-Means (FCM), and Z-score normalization for pre-processing the BraTS (Brain Tumor Segmentation Challenge) 2018 dataset. We utilized three variants of U-Net architecture, convolutional block attention module (CBAM), squeeze and excitation module (SEM), and refinement module (RM) for the segmentation of the ROIs. Utilizing Z-score performs better than other normalization techniques for tumor core (TC) and whole tumor (WT) segmentation. In contrast, FCM performs superior to the other two normalization techniques on enhancement tumor (ET) segmentation.

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Availability of data and material

This article does not contain any studies with human participants or animals performed by any of the authors. All the database is acquired from the public logging system (Internet source) whose appropriate references are added in the sections above.

Code Availability

Any public available code is cited in the text at appropriate places and the novel code for the work can taken from the authors upon request.

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Acknowledgments

The author Rahul Kumar would like to thank for support through the “Visvesvaraya Ph.D. Scheme for Electronics & IT” by the Ministry of Electronics & Information Technology (MeitY), Govt. of India, to carry out this research.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Indian Institute of Technology Roorkee, Roorkee, India

    Rahul Kumar, Ankur Gupta & Balasubramanian Raman

  2. Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee, India

    Harkirat Singh Arora

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  1. Rahul Kumar
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Correspondence to Rahul Kumar.

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Rahul Kumar and Ankur Gupta contributed equally to this work.

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Kumar, R., Gupta, A., Arora, H.S. et al. CBSN: Comparative measures of normalization techniques for brain tumor segmentation using SRCNet. Multimed Tools Appl 81, 13203–13235 (2022). https://doi.org/10.1007/s11042-021-10565-0

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